The major purpose of the proposed research is the study of information-processing in human judgment and decision-making in probabilistic environments. Such tasks are commonly called multiple-cue probability learning tasks. The tasks to be analyzed are those in which the subject must learn to make judgements on the basis of cues or information available to him at the time the particular judgment is made. In general, such cues are related probabilistically to the criterion to be predicted as in clinical judgment tasks. Research will focus on the manner in which subjects learn to make judgments in such tasks. Specific attention will be paid to the structural characteristics of the task environment. Major factors to be studied include the effect of variable base rate of the criterion, the effect of changes or drift in cue validities, the effect of dependencies between cue dimensions, and the effect of cue deprivation or omission. The form and quality of subject feedback will be an important feature of the research. Different sorts of feedback will be employed, not only to determine the manner in which information is processed, but to determine the conditions under which the quality of judgment can be improved or the rate of learning increased. In general, the tasks will be discrete in that there will be only a few judgment categories (usually two). Cue dimensions will be discrete as well, but the dimensionality of the cues will play an important role. Hypothesis generation models for judgment in multiple-cue situations will be analyzed and tested in terms of empirical data. Response measures include subject response proportions conditional upon cues, accuracy measures, and response times. Individual differences in judgment will be systematically considered.